A Convex Reconstruction Model for X-Ray Tomographic Imaging With Uncertain Flat-Fields

Aggrawal, Hari Om; Andersen, Martin S.; Rose, Sean D.; Sidky, Emil Y.

doi:10.1109/TCI.2017.2723246

Title: A Convex Reconstruction Model for X-Ray Tomographic Imaging With Uncertain Flat-Fields

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Abstract

Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time is sufficiently high, this kind of uncertainty typically has a negligible effect on the reconstruction quality. However, in time- or dose-limited applications such as dynamic CT, this uncertainty may cause severe and systematic artifacts known as ring artifacts. By carefully modeling the measurement process and by taking uncertainties into account, we derive a new convex model that leads to improved reconstructions despite poor quality measurements. We demonstrate the effectiveness of the methodology based on simulated and real datasets.

Authors:

;

Publication Date:: 2018-03-01

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division; National Science Foundation (NSF); European Research Council (ERC); National Institutes of Health (NIH)

OSTI Identifier:: 1420219

Alternate Identifier(s):: OSTI ID: 1420220; OSTI ID: 1434728

Grant/Contract Number:: AC02-06CH11357; FG02-94ER14466; EAR-1128799; 291405; CA158446

Resource Type:: Published Article

Journal Name:: IEEE Transactions on Computational Imaging

Additional Journal Information:: Journal Name: IEEE Transactions on Computational Imaging Journal Volume: 4 Journal Issue: 1; Journal ID: ISSN 2573-0436

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 42 ENGINEERING; low intensity; reconstruction methods; ring artifacts; x-ray computed tomography

Citation Formats


                    Aggrawal, Hari Om, Andersen, Martin S., Rose, Sean D., and Sidky, Emil Y. A Convex Reconstruction Model for X-Ray Tomographic Imaging With Uncertain Flat-Fields.  United States: N. p., 2018. 
Web.  doi:10.1109/TCI.2017.2723246.

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                    Aggrawal, Hari Om, Andersen, Martin S., Rose, Sean D., & Sidky, Emil Y. A Convex Reconstruction Model for X-Ray Tomographic Imaging With Uncertain Flat-Fields.  United States.  https://doi.org/10.1109/TCI.2017.2723246

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                    Aggrawal, Hari Om, Andersen, Martin S., Rose, Sean D., and Sidky, Emil Y. Thu .  
"A Convex Reconstruction Model for X-Ray Tomographic Imaging With Uncertain Flat-Fields".  United States.  https://doi.org/10.1109/TCI.2017.2723246.

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@article{osti_1420219,

  title        = {A Convex Reconstruction Model for X-Ray Tomographic Imaging With Uncertain Flat-Fields},

  author       = {Aggrawal, Hari Om and Andersen, Martin S. and Rose, Sean D. and Sidky, Emil Y.},

  abstractNote = {Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time is sufficiently high, this kind of uncertainty typically has a negligible effect on the reconstruction quality. However, in time- or dose-limited applications such as dynamic CT, this uncertainty may cause severe and systematic artifacts known as ring artifacts. By carefully modeling the measurement process and by taking uncertainties into account, we derive a new convex model that leads to improved reconstructions despite poor quality measurements. We demonstrate the effectiveness of the methodology based on simulated and real datasets.},

  doi          = {10.1109/TCI.2017.2723246},

  journal      = {IEEE Transactions on Computational Imaging},

  number       = 1,

  volume       = 4,

  place        = {United States},

  year         = {2018},

  month        = {3}

}

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